Recommender Systems Handbook [electronic resource] /
Ricci, Francesco.editor.
Rokach, Lior.lateditor.
Shapira, Bracha.lateditor.
Kantor, Paul B.lateditor.
The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.
Description based upon print version of record.
Includes bibliographical references and index.
Introduction to Recommender Systems Handbook -- Part I Basic Techniques -- Data Mining Methods for Recommender Systems -- Content-based Recommender Systems: State of the Art and Trends -- A Comprehensive Survey of Neighborhood-based Recommendation Methods -- Advances in Collaborative Filtering -- Developing Constraint-based Recommenders -- Context-Aware Recommender Systems -- Part II Applications and Evaluation of RSs -- Evaluating Recommendation Systems -- A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment -- How to Get the Recommender Out of the Lab? -- Matching Recommendation Technologies and Domains -- Recommender Systems in Technology Enhanced Learning -- Part III Interacting with Recommender Systems -- On the Evolution of Critiquing Recommenders -- Creating More Credible and Persuasive Recommender Systems: The Influence of Source Characteristics on Recommender System Evaluations -- Designing and Evaluating Explanations for Recommender Systems -- Usability Guidelines for Product Recommenders Based on Example Critiquing Research -- Map Based Visualization of Product Catalogs -- Part IV Recommender Systems and Communities -- Communities, Collaboration, and Recommender Systems in Personalized Web Search -- Social Tagging Recommender Systems -- Trust and Recommendations -- Group Recommender Systems: Combining Individual Models -- Aggregation of Preferences in Recommender Systems -- Active Learning in Recommender Systems -- Multi-Criteria Recommender Systems -- Robust Collaborative Recommendation -- Index.
The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.
Artificial intelligence.
Information storage and retrieval.
Data mining.
E-commerce.
User interfaces (Computer systems).
Database management.
Artificial Intelligence.
Information Storage and Retrieval.
Data Mining and Knowledge Discovery.
e-Commerce/e-business.
User Interfaces and Human Computer Interaction.
Database Management.